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A trade-off between accuracy and fairness is almost taken as a given in the existing literature on fairness in machine learning.
On a measure of divergence between two multinomial populations
Anil Bhattacharyya · 1946
Earlier work this paper cites.
The divergence and Bhattacharyya distance measures in signal selection
Thomas Kailath · 1967
Earlier work this paper cites.
Randomized Algorithms
Rajeev Motwani and Prabhakar Raghavan · 1995
Earlier work this paper cites.
A kernel method for the two-sample-problem
Arthur Gretton, Karsten Borgwardt, Malte Rasch, Bernhard Schölkopf, and Alex J. Smola · 2007
Earlier work this paper cites.
Sparse additive models
Pradeep Ravikumar, John Lafferty, Han Liu, and Larry Wasserman · 2009
Earlier work this paper cites.
The balanced accuracy and its posterior distribution
Kay Henning Brodersen, Cheng Soon Ong, Klaas Enno Stephan, and Joachim M Buhmann · 2010
Earlier work this paper cites.
Fairness through awareness
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel · 2012
Earlier work this paper cites.
Generalized Chernoff information for mismatched Bayesian detection and its application to energy detection
Yuni Lee and Youngchul Sung · 2012
Earlier work this paper cites.
Elements of Information Theory
Thomas M. Cover and Joy A. Thomas · 2012
Earlier work this paper cites.
A Chernoff-type lower bound for the Gaussian Q-function
François D Côté, Ioannis N Psaromiligkos, and Warren J Gross · 2012
Earlier work this paper cites.
Data preprocessing techniques for classification without discrimination
Faisal Kamiran and Toon Calders · 2012
Earlier work this paper cites.
Detection, decisions, and hypothesis testing
RG Gallager · 2012
Earlier work this paper cites.
Learning fair representations
Rich Zemel, Yu Wu, Kevin Swersky, Toni Pitassi, and Cynthia Dwork · 2013
Earlier work this paper cites.
Classification with asymmetric label noise: Consistency and maximal denoising
Clayton Scott, Gilles Blanchard, and Gregory Handy · 2013
Earlier work this paper cites.
Ensemble estimators for multivariate entropy estimation
K. Sricharan, D. Wei, and A. O. Hero · 2013
Earlier work this paper cites.
Concentration inequalities: A nonasymptotic theory of independence
Stéphane Boucheron, Gábor Lugosi, and Pascal Massart · 2013
Earlier work this paper cites.
Certifying and removing disparate impact
Michael Feldman, Sorelle A Friedler, John Moeller, Carlos Scheidegger, and Suresh Venkatasubramanian · 2015
Cited alongside, same era.
A finite sample analysis of the naive Bayes classifier
Daniel Berend and Aryeh Kontorovich · 2015
Cited alongside, same era.
Empirically estimable classification bounds based on a nonparametric divergence measure
Visar Berisha, Alan Wisler, Alfred O Hero, and Andreas Spanias · 2015
Cited alongside, same era.
Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, and Nathan Srebro · 2016
Cited alongside, same era.
On the (im)possibility of fairness
Sorelle A Friedler, Carlos Scheidegger, and Suresh Venkatasubramanian · 2016
Cited alongside, same era.
Empirically-estimable multi-class classification bounds
Why interpretability in machine learning? An answer using distributed detection and data fusion theory
Kush R. Varshney, Prashant Khanduri, Pranay Sharma, Shan Zhang, and Pramod K. Varshney · 2018
Later among the works it cites.
Decoupled classifiers for group-fair and efficient machine learning
Cynthia Dwork, Nicole Immorlica, Adam Tauman Kalai, and Max Leiserson · 2018
Later among the works it cites.
Empirical risk minimization under fairness constraints
Michele Donini, Luca Oneto, Shai Ben-David, John S Shawe-Taylor, and Massimiliano Pontil · 2018
Later among the works it cites.
Multi-class bayes error estimation with a global minimal spanning tree
S. Y. Sekeh, B. Oselio, and A. O. Hero · 2018
Later among the works it cites.
Inherent tradeoffs in learning fair representation
Han Zhao and Geoffrey J Gordon · 2019
Closest in time.
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A. Wisler, V. Berisha, D. Wei, K. Ramamurthy, and A. Spanias · 2016
Cited alongside, same era.
Optimized pre-processing for discrimination prevention
Flavio P. Calmon, Dennis Wei, Bhanukiran Vinzamuri, Karthikeyan Natesan Ramamurthy, and Kush R. Varshney · 2017
Cited alongside, same era.
Counterfactual fairness
Matt J Kusner, Joshua Loftus, Chris Russell, and Ricardo Silva · 2017
Cited alongside, same era.
Avoiding discrimination through causal reasoning
Niki Kilbertus, Mateo Rojas Carulla, Giambattista Parascandolo, Moritz Hardt, Dominik Janzing, and Bernhard Schölkopf · 2017
Cited alongside, same era.
Fairness beyond disparate treatment & disparate impact: Learning classification without disparate mistreatment
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez, and Krishna P Gummadi · 2017
Cited alongside, same era.
A reductions approach to fair classification
Alekh Agarwal, Alina Beygelzimer, Miroslav Dudík, John Langford, and Hanna Wallach · 2018
Cited alongside, same era.
Fairness in supervised learning: An information theoretic approach
AmirEmad Ghassami, Sajad Khodadadian, and Negar Kiyavash · 2018
Cited alongside, same era.
Active fairness in algorithmic decision making
Alejandro Noriega-Campero, Michiel A. Bakker, Bernardo Garcia-Bulle, and Alex ’Sandy’ Pentland · 2019
Closest in time.
On fairness in budget-constrained decision making
Michiel A. Bakker, Alejandro Noriega-Campero, Duy Patrick Tu, Prasanna Sattigeri, Kush R. Varshney, and Alex ’Sandy’ Pentland · 2019
Closest in time.
Tracking and improving information in the service of fairness
Sumegha Garg, Michael P. Kim, and Omer Reingold · 2019
Closest in time.
Recovering from biased data: Can fairness constraints improve accuracy?, 2019
Avrim Blum and Kevin Stangl · 2019
Closest in time.
Unlocking fairness: a trade-off revisited
Michael Wick, Swetasudha Panda, and Jean-Baptiste Tristan · 2019
Closest in time.
Classification with fairness constraints: A meta-algorithm with provable guarantees
L. Elisa Celis, Lingxiao Huang, Vijay Keswani, and Nisheeth K. Vishnoi · 2019
Closest in time.
Identifying and correcting label bias in machine learning
Heinrich Jiang and Ofir Nachum · 2019
Closest in time.
Bounding the fairness and accuracy of classifiers from population statistics
Sivan Sabato and Elad Yom-Tov · 2020
Closest in time.
Model-agnostic characterization of fairness trade-offs
Joon Sik Kim, Jiahao Chen, and Ameet Talwalkar · 2020
Closest in time.
Data augmentation for discrimination prevention and bias disambiguation
Shubham Sharma, Yunfeng Zhang, Jesús M Ríos Aliaga, Djallel Bouneffouf, Vinod Muthusamy, and Kush R Varshney · 2020
Closest in time.
On fair selection in the presence of implicit variance
Vitalii Emelianov, Nicolas Gast, Krishna P Gummadi, and Patrick Loiseau · 2020
Closest in time.